Type:	Package
Title:	MASCOTNUM / RT-UQ Algorithms Template Tools
Version:	0.2-1
Date:	2025-07-25
Author:	Yann Richet [aut, cre]
Maintainer:	Yann Richet <yann.richet@asnr.fr>
Description:	Helper functions for MASCOTNUM / RT-UQ https://uq.math.cnrs.fr/ algorithm template, for design of numerical experiments practice: algorithm template parser to support MASCOTNUM specification https://github.com/MASCOTNUM/algorithms, 'ask & tell' decoupling injection (inspired by https://search.r-project.org/CRAN/refmans/sensitivity/html/decoupling.html) to use "crimped" algorithms (like uniroot(), optim(), ...) from outside R, basic template examples: Brent algorithm for 1 dim root finding and L-BFGS-B from base optim().
License:	Apache License (≥ 2)
Encoding:	UTF-8
Depends:	R (≥ 4.0)
Imports:	utils, stats, remotes, xml2, jsonlite
Suggests:	testthat, future
URL:	https://github.com/MASCOTNUM/templr
RoxygenNote:	7.2.1
NeedsCompilation:	no
Packaged:	2025-07-25 09:05:08 UTC; richet
Repository:	CRAN
Date/Publication:	2025-07-26 06:00:02 UTC

ask&tell component function to 'ask' where objective function evaluation is required.

Description

ask&tell component function to 'ask' where objective function evaluation is required.

Usage

ask_X(
  id = 0,
  X.tmp = "X.todo",
  tmp_path = file.path(tempdir(), "..", "asktell.tmp"),
  sleep_step = 0.1,
  sleep_init = 0.1,
  timeout = 360000,
  trace = function(...) cat(paste0(..., "\n")),
  clean = TRUE
)

Arguments

Details

'ask&tell' injection loop to call an external objective function within an inline algorithm (like optim(...)) Main idea: pass 'ask_Y' as objectve function argument of algorithm, which will wait until you call 'tell_Y' in another R process. In this secondary process, you can read what X is called using 'ask_X', and when you know what values returns from the external objective, just call 'tell_Y' to give it.

Value

input value of objective function to compute externally

Author(s)

Y. Richet, discussions with D. Sinoquet. Async IO principle was defined by G. Pujol.

Examples

## Not run:  ### Assumes you can use two independent R sessions
## In main R session
  ask_Y(x=123)
## In another R session
  ask_X() # returns 123
  tell_Y(y=456)
## Then ask_dY in main R session returns with value '456'

## End(Not run)

ask&tell component function to 'ask' objective function evaluation.

Description

ask&tell component function to 'ask' objective function evaluation.

Usage

ask_Y(
  x,
  id = 0,
  X.tmp = "X.todo",
  Y.tmp = "Y.done",
  tmp_path = file.path(tempdir(), "..", "asktell.tmp"),
  sleep_step = 0.1,
  sleep_init = 0,
  timeout = 360000,
  trace = function(...) cat(paste0(..., "\n")),
  clean = TRUE,
  force_cleanup = FALSE
)

Arguments

Details

'ask&tell' injection loop to call an external objective function within an inline algorithm (like optim(...)) Main idea: pass 'ask_Y' as objectve function argument of algorithm, which will wait until you call 'tell_Y' in another R process. In this secondary process, you can read what X is called using 'ask_X', and when you know what values returns from the external objective, just call 'tell_Y' to give it.

Value

output value of objective function, as given by tell_Y() call in parallel session

Author(s)

Y. Richet, discussions with D. Sinoquet. Async IO principle was defined by G. Pujol.

Examples

## Not run:  ### Assumes you can use two independent R sessions
## In main R session
  ask_Y(x=123)
## In another R session
  ask_X() # returns 123
  tell_Y(y=456)
## Then ask_Y in main R session returns with value '456'

## End(Not run)

ask&tell component function to 'ask' where objective function gradient evaluation is required.

Description

ask&tell component function to 'ask' where objective function gradient evaluation is required.

Usage

ask_dX(
  id = 0,
  dX.tmp = "dX.todo",
  tmp_path = file.path(tempdir(), "..", "asktell.tmp"),
  sleep_step = 0.1,
  sleep_init = 0,
  timeout = 360000,
  trace = function(...) cat(paste0(..., "\n")),
  clean = TRUE
)

Arguments

Details

'ask&tell' injection loop to call an external objective function within an inline algorithm (like optim(...)) Main idea: pass 'ask_Y' as objectve function argument of algorithm, which will wait until you call 'tell_Y' in another R process. In this secondary process, you can read what X is called using 'ask_X', and when you know what values returns from the external objective, just call 'tell_Y' to give it.

Value

input values of objective function to compute externally

Author(s)

Y. Richet, discussions with D. Sinoquet. Async IO principle was defined by G. Pujol.

Examples

## Not run:  ### Assumes you can use two independent R sessions
## In main R session
  ask_dY(x=123)
## In another R session
  ask_dX() # returns 123
  tell_dY(y=456)
## Then ask_dY in main R session returns with value '456'

## End(Not run)

ask&tell component function to 'ask' objective function gradient evaluation using finite difference.

Description

ask&tell component function to 'ask' objective function gradient evaluation using finite difference.

Usage

ask_dY(
  x,
  dX = 0.001,
  id = 0,
  dX.tmp = "dX.todo",
  dY.tmp = "dY.done",
  tmp_path = file.path(tempdir(), "..", "asktell.tmp"),
  sleep_step = 0.1,
  sleep_init = 0,
  timeout = 360000,
  trace = function(...) cat(paste0(..., "\n")),
  clean = TRUE,
  force_cleanup = FALSE
)

Arguments

Details

'ask&tell' injection loop to call an external objective function within an inline algorithm (like optim(...)) Main idea: pass 'ask_Y' as objectve function argument of algorithm, which will wait until you call 'tell_Y' in another R process. In this secondary process, you can read what X is called using 'ask_X', and when you know what values returns from the external objective, just call 'tell_Y' to give it.

Value

output value of objective function gradient, as given by tell_dY() call in parallel session

Author(s)

Y. Richet, discussions with D. Sinoquet. Async IO principle was defined by G. Pujol.

Examples

## Not run:  ### Assumes you can use two independent R sessions
## In main R session
  ask_dY(x=123)
## In another R session
  ask_dX() # returns 123
  tell_dY(y=456)
## Then ask_dY in main R session returns with value '456'

## End(Not run)

Helper function to scale from [0,1] to [min,max]

Description

Helper function to scale from [0,1] to [min,max]

Usage

from01(X, inp)

Arguments

Value

X scaled in [inp$min, inp$max]

Examples

from01(data.frame(x=matrix(runif(10))),list(x=list(min=10,max=20)))

Dependencies loader, supports many protocols like github:, gitlab:, ... using remotes::instal_... Will create a local '.lib' directory to store packages installed

Description

Dependencies loader, supports many protocols like github:, gitlab:, ... using remotes::instal_... Will create a local '.lib' directory to store packages installed

Usage

import(..., lib.loc = NULL, trace = function(...) cat(paste0(..., "\n")))

Arguments

Value

(list of) load status of packages (TRUE/FALSE)

Examples

if(interactive()){
  import('VGAM')
}

Parse algorithm string result in R list

Description

Parse algorithm string result in R list

Usage

list.results(result)

Arguments

Value

list of string parsed: extract XML or JSON content

Examples

 list.results(paste0(
  "<HTML name='minimum'>minimum is 0.523431237543406 found at ...</HTML>",
  "<min> 0.523431237543406 </min>",
  "<argmin>[ 0.543459029033452,0.173028395040855 ]</argmin>"))

Helper function to get $max from 'input' list

Description

Helper function to get $max from 'input' list

Usage

max_input(inp)

Arguments

Value

array of inp$...$max values

Examples

max_input(list(x1=list(min=0,max=1),x2=list(min=2,max=3)))

Helper function to get $min from 'input' list

Description

Helper function to get $min from 'input' list

Usage

min_input(inp)

Arguments

Value

array of inp$...$min values

Examples

min_input(list(x1=list(min=0,max=1),x2=list(min=2,max=3)))

Parse algorithm file and returns its (header) indos and methods

Description

Parse algorithm file and returns its (header) indos and methods

Usage

parse.algorithm(file)

Arguments

Value

list of header infos and environment containing methods <constructor>,getInitialDesign,getNextDesign,displayResults

Examples

parse.algorithm(system.file("Brent.R", package="templr"))

Read algorithm file and returns one header info

Description

Read algorithm file and returns one header info

Usage

read.algorithm(file, info = "help")

Arguments

Value

list of header infos

Examples

read.algorithm(system.file("Brent.R", package="templr"),"help")

Apply a template algorithm file to an objective function

Description

Apply a template algorithm file to an objective function

Usage

run.algorithm(
  algorithm_file,
  objective_function,
  input,
  output = NULL,
  options = NULL,
  work_dir = ".",
  trace = function(...) cat(paste0(..., "\n")),
  silent = FALSE,
  save_data = TRUE
)

Arguments

Value

algorithm result (and algorithm object & files as attributes)

Examples

run.algorithm(
  system.file("Brent.R", package="templr"),
  function(x) sin(x)-0.75,
  list(x=list(min=0,max=pi/2)),
  work_dir=tempdir()
  )

ask&tell component function to 'tell' objective function value to waiting 'ask_Y' call in another R session.

Description

ask&tell component function to 'tell' objective function value to waiting 'ask_Y' call in another R session.

Usage

tell_Y(
  y,
  id = 0,
  Y.tmp = "Y.done",
  tmp_path = file.path(tempdir(), "..", "asktell.tmp"),
  trace = function(...) cat(paste0(..., "\n")),
  force_cleanup = FALSE
)

Arguments

Details

'ask&tell' injection loop to call an external objective function within an inline algorithm (like optim(...)) Main idea: pass 'ask_Y' as objectve function argument of algorithm, which will wait until you call 'tell_Y' in another R process. In this secondary process, you can read what X is called using 'ask_X', and when you know what values returns from the external objective, just call 'tell_Y' to give it.

Value

input value of objective function to compute externally

Author(s)

Y. Richet, discussions with D. Sinoquet. Async IO principle was defined by G. Pujol.

Examples

## Not run:  ### Assumes you can use two independent R sessions
## In main R session
  ask_Y(x=123)
## In another R session
  ask_X() # returns 123
  tell_Y(y=456)
## Then ask_dY in main R session returns with value '456'

## End(Not run)

ask&tell component function to 'tell' objective function value to waiting 'ask_Y' call in another R session.

Description

ask&tell component function to 'tell' objective function value to waiting 'ask_Y' call in another R session.

Usage

tell_dY(
  dy,
  id = 0,
  dY.tmp = "dY.done",
  tmp_path = file.path(tempdir(), "..", "asktell.tmp"),
  trace = function(...) cat(paste0(..., "\n")),
  force_cleanup = FALSE
)

Arguments

Details

'ask&tell' injection loop to call an external objective function within an inline algorithm (like optim(...)) Main idea: pass 'ask_Y' as objectve function argument of algorithm, which will wait until you call 'tell_Y' in another R process. In this secondary process, you can read what X is called using 'ask_X', and when you know what values returns from the external objective, just call 'tell_Y' to give it.

Value

input value of objective function to compute externally

Author(s)

Y. Richet, discussions with D. Sinoquet. Async IO principle was defined by G. Pujol.

Examples

## Not run:  ### Assumes you can use two independent R sessions
## In main R session
  ask_dY(x=123)
## In another R session
  ask_dX() # returns c(123, 123.123)
  tell_dY(dy=c(456,456.123))
## Then ask_dY in main R session returns with value '1'

## End(Not run)

Helper function to scale from [min,max] to [0,1]

Description

Helper function to scale from [min,max] to [0,1]

Usage

to01(X, inp)

Arguments

Value

X scaled in [0,1]

Examples

to01(10+10*data.frame(x=matrix(runif(10))),list(x=list(min=10,max=20)))